Four-dimensional treatment planning strategies for dynamic tumor tracking.

INTRODUCTION: Dynamic tumor tracking (DTT) is a motion management technique where the radiation beam follows a moving tumor in real time. Not modelling DTT beam motion in the treatment planning system leaves an organ at risk (OAR) vulnerable to exceeding its dose limit. This work investigates two planning strategies for DTT plans, the "Boolean OAR Method" and the "Aperture Sorting Method," to determine if they can successfully spare an OAR while maintaining sufficient target coverage. MATERIALS AND METHODS: A step-and-shoot intensity modulated radiation therapy (sIMRT) treatment plan was re-optimized for 10 previously treated liver stereotactic ablative radiotherapy patients who each had one OAR very close to the target. Two planning strategies were investigated to determine which is more effective at sparing an OAR while maintaining target coverage: (1) the "Boolean OAR Method" created a union of an OAR's contours from two breathing phases (exhale and inhale) on the exhale phase (the planning CT) and protected this combined OAR during plan optimization, (2) the "Aperture Sorting Method" assigned apertures to the breathing phase where they contributed the least to an OAR's maximum dose. RESULTS: All 10 OARs exceeded their dose constraints on the original plan four-dimensional (4D) dose distributions and average target coverage was V100% = 91.3% ± 2.9% (ranging from 85.1% to 94.8%). The "Boolean OAR Method" spared 7/10 OARs, and mean target coverage decreased to V100% = 87.1% ± 3.8% (ranging from 80.7% to 93.7%). The "Aperture Sorting Method" spared 9/10 OARs and the mean target coverage remained high at V100% = 91.7% ± 2.8% (ranging from 84.9% to 94.5%). CONCLUSIONS: 4D planning strategies are simple to implement and can improve OAR sparing during DTT treatments. The "Boolean OAR Method" improved sparing of OARs but target coverage was reduced. The "Aperture Sorting Method" further improved sparing of OARs and maintained target coverage.

Four-dimensional dose calculations for dynamic tumour tracking with a gimbal-mounted linear accelerator.

PURPOSE: In this study we present a novel method for re-calculating a treatment plan on different respiratory phases by accurately modeling the panning and tilting beam motion during DTT (the "rotation method"). This method is used to re-calculate the dose distribution of a plan on multiple breathing phases to accurately assess the dosimetry. METHODS: sIMRT plans were optimized on a breath hold computed tomography (CT) image taken at exhale (BHexhale ) for 10 previous liver stereotactic ablative radiotherapy patients. Our method was used to re-calculate the plan on the inhale (0%) and exhale (50%) phases of the four-dimensional CT (4DCT) image set. The dose distributions were deformed to the BHexhale CT and summed together with proper weighting calculated from the patient's breathing trace. Subsequently, the plan was re-calculated on all ten phases using our method and the dose distributions were deformed to the BHexhale CT and accumulated together. The maximum dose for certain organs at risk (OARs) was compared between calculating on two phases and all ten phases. RESULTS: In total, 26 OARs were examined from 10 patients. When the dose was calculated on the inhale and exhale phases six OARs exceeded their dose limit, and when all 10 phases were used five OARs exceeded their limit. CONCLUSION: Dynamic tumor tracking plans optimized for a single respiratory phase leave an OAR vulnerable to exceeding its dose constraint during other respiratory phases. The rotation method accurately models the beam's geometry. Using deformable image registration to accumulate dose from all 10 breathing phases provides the most accurate results, however it is a time consuming procedure. Accumulating the dose from two extreme breathing phases (exhale and inhale) and weighting them properly provides accurate results while requiring less time. This approach should be used to confirm the safety of a DTT treatment plan prior to delivery.